Conventional Von Neumann type computers use location addressable memory and sequential processing to store and manipulate data. These computers process information using a series of programmed steps and identify data based on its stored location in the system. They often cannot solve problems requiring analysis of very "noisy" data to an acceptable level of confidence or cannot solve them quickly enough to be useful.
The failure to efficiently discern internal consistencies in confusing, incomplete and/or extraneous data is at the heart of many of the current limitations in computer technology, particularly as it relates to artificial intelligence and pattern recognition problems. In its broadest sense, pattern recognition involves the ability to differentiate or classify previously unencountered data based upon relative comparisons to known data. The data itself can take on any form, i.e., audio, visual, or the like, and the noise polluting the signal will be variations or distortions of the data. For example, variations in accent, pitch, or harmonic content, can make the computer analysis of human speech difficult.
Thus, there is a need for a system which can efficiently and reliably analyze and classify previously unseen and/or noisy data. To be useful as a pattern recognizer, the system should be able to generate outputs representing the degree of similarity between an unknown pattern and each of several reference patterns or sets of reference patterns. Moreover, the system should be "adaptable", so that incorrect generalizations may be corrected and a desired level of confidence attained.